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The position A position in knowledge-driven machine learning is available at the Department of Physics and Technology , Faculty of Science and Technology, within the UiT Machine Learning Group . This position is
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development for a hard-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and
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particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
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performance, plume evolution, and pressure-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites
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Stig Brøndbo 22nd October 2025 Languages English English English Faculty of Science and Technology PhD Fellow in Knowledge-Driven Machine Learning Apply for this job See advertisement The position A
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Norwegian courses. Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms
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– the Norwegian Centre for Knowledge-driven Machine Learning. We are looking for a motivated candidate, who has interest in both theoretical, methodological and applied research in anomaly detection in sequential
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and iteratively improved. • Integrate and test autonomy stacks (perception, learning, planning) on physical robots. • Use evolutionary algorithms to optimize both the robot’s body and brain
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, geometric deep learning. Considered an advantage: experience in programming or course work in computer science, algebra, topology or differential geometry, knowledge of topological data analysis or machine
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of partial differential equations (PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in